Data Science and Business Intelligence: 8 Differences

Harsh Varshney • Last Modified: December 29th, 2022

Data Science and Business Intelligence | Hevo Data

Data Collection is a reasonably well-established technique. In fact, forward-thinking companies began collecting data before determining how they would use it. Even if they didn’t know how to extract that value, they knew that data was valuable. The question now is how to turn that data into actionable Business Intelligence.

Data Science and Business Intelligence professionals are increasingly concentrating their efforts on figuring out how to make the most of all the data. Data is becoming more complicated in terms of volume, velocity, and variety. New Structured and Unstructured Data Sources from the Cloud and SaaS apps must be integrated with legacy on-premises data warehouses. The requirement to make judgments in real-time necessitates speedier Data Collection and Processing.

This article will show you Data Science and Business Intelligence. It will also provide a deep comparison of Data Science and Business Intelligence.

Table of Contents

What is Data Science?

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While there is no globally accepted definition of Data Science, it is widely recognized as a field that incorporates a variety of disciplines, such as Statistics, Advanced Programming Abilities, and Machine Learning, to derive actionable insights from raw data.

Data Science is the process of extracting value from a company’s data in order to address complicated problems. It’s vital to remember that data science is still a young profession, and this definition is constantly changing.

Why Data Science is Important?

Companies may foresee, prepare, and optimize their operations using data science as a guide. Furthermore, data science is critical to the user experience; many organizations rely on data science to provide personalized and personalized services. Streaming services like Netflix and Hulu, for example, might propose entertainment based on a user’s previous viewing history and taste preferences. Subscribers spend less time looking for something to watch and may quickly find value among the hundreds of options, resulting in a unique and personalized experience. This is significant since it improves subscriber convenience while also increasing client retention.

What are the Key Use Cases of Data Science?

  • Healthcare: In the healthcare industry, data science has led to a number of advances. Medical professionals are discovering new ways to study disease, practice preventative medicine, diagnose diseases faster, and explore new treatment options thanks to a massive network of data now available via anything from EMRs to clinical databases to personal activity monitors. By evaluating medical test data and reported symptoms, Data Science enhances patient diagnosis at an early stage and helps them in treating more effectively.
  • Cybersecurity: Kaspersky Lab, an international cybersecurity organization, uses Data Science and Machine Learning to detect approximately 360,000 new malware samples every day. Data science’s ability to detect and understand new ways of cybercrime in real time is critical to our future safety and security.
  • Airlines: Another good example is the airline industry, which can use Data Science to improve operations in a variety of ways, such as planning routes and deciding whether to schedule direct or connecting flights, building Predictive Analytics Models to forecast flight delays, offering personalized promotional offers based on customers’ booking patterns, and deciding which class of planes to buy for better overall performance.
  • Banks: For example, if you provide money on credit, the likelihood of clients making future credit payments on schedule is a problem for you. Here, you may create a model that uses Predictive Analytics to predict whether future payments will be on time or not based on the customer’s payment history.
  • Police: For example, a metropolitan police agency developed Statistical Incident Analysis techniques to assist officers in determining when and where to deploy resources to prevent crime. The data-driven technology generates reports and dashboards to help field officers improve their situational awareness.

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What is Business Intelligence?

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To aid decision-making, Business Intelligence is the process of producing and sharing strategic insights based on existing corporate data. The goal of Business Intelligence is to give you a comprehensive picture of your company’s current and historical data. When Business Intelligence (BI) was initially introduced in the early 1960s, it was intended to be a tool for sharing information between departments. BI has grown into complex Data Analysis procedures since then, but communication has remained at its foundation.

Furthermore, BI encompasses not only the procedures and methods for analyzing data and answering specific business questions but also the tools that support those methods. These self-service solutions enable customers to swiftly visualize and comprehend corporate data.

Why Business Intelligence is Important?

Business Intelligence is more important than ever in delivering a full snapshot of business information since data quantities are continually expanding. This provides direction for making educated decisions and finding areas for development, resulting in increased organizational efficiency and a higher bottom line.

By displaying current and historical data within the context of their business, Business Intelligence can assist firms in making better decisions. Analysts can use BI to give performance and competitive benchmarks, allowing the company to run more smoothly and efficiently. Analysts can also notice market trends more easily, which can help enhance sales or revenue. When used correctly, data may assist with everything from compliance to employment initiatives. Here are a few examples of how Business Intelligence may assist firms in making better, data-driven decisions:

  • Predicting Success
  • Spotting Market Trends
  • Discovering Issues or Problems
  • Identifying Ways to Increase Profit
  • Analyzing Customer Behavior
  • Comparing Data with Competitors
  • Tracking Performance
  • Optimizing Operations

What are the Key Use Cases of Business Intelligence?

  • Advanced Analytics: Predictive Models aim to anticipate the future, whereas Traditional Analytics is the act of leveraging Historical Data to make more informed judgments in the future. Organizations that utilize Predictive Tools end up building a simulation of future conditions in order to run through various scenarios and reach conclusions ahead of their competitors. The granularity of the analysis and the types of assumptions used determine the Accuracy and usability of Predictive Analytics.
  • Cloud Analytics: The Cloud has wreaked havoc on a number of critical enterprise software areas, none more so than Business Intelligence and Data Management. You can expect this trend to continue as Cloud technologies become more widely adopted. Organizations looking for Cloud BI and Analytics technologies that support hybrid and multi-cloud deployment methodologies fall into this Business Intelligence use case. Data Connectivity is a crucial factor, just as it is in the self-service use case. Governance and Security are also important.
  • Augmented Analytics: Machine Learning is utilized in Augmented Analytics to transform the way analytic content is created and used. Other modern analytical capabilities covered by the technology include Data Preparation, Data Management, Business Process Management, Process Mining, and Data Science. Organizations can also include Augmented Analytics findings in their own apps. These operations are automated using Augmented Analytics, which eliminates the need for data scientists.
  • Embedded Analytics: Embedded Analytics software incorporates analytic capabilities into a commercial application. To make Data Analysis more convenient, several self-service BI platforms allow users to embed analytic dashboards into regularly used apps. By incorporating analytics into existing workflows, business users can obtain access to the capabilities they require without having to leave the settings they operate in every day. Users are frequently rewarded with more actionable insight as a result of faster, more educated, and more efficient decision-making.
  • Self-service Analytics: Self-service Analytics allows non-technical users (such as Business Analysts) to connect directly to a variety of data sources in order to analyze and visualize blended datasets. Data Governance is also a component of successful self-service methods. This is because Data Governance ensures the Accuracy and Quality Control of the information given. Users are encouraged to develop partnerships in order to ensure that data is available and accurate. For efficient Report Consumption and Preparation, business definitions must also be properly written up.

Understanding Data Science and Business Intelligence Comparison: 8 Differences

Data Science and Business Intelligence Differences: Data Types

Business Intelligence uses Structured data that is often stored in data warehouses or silos. Similarly, while Data Science works with structured data, it is primarily charged with Unstructured and Semi-Structured Data, requiring more time to clean and improve data quality.

Data Science and Business Intelligence Differences: Perspective

Business Intelligence is concerned with the present, whereas Data Science is concerned with the future and forecasting what may occur. Data Science produces predictive models that anticipate future opportunities, whereas BI works with historical data to determine a responsive course of action.

Data Science and Business Intelligence Differences: Deliverables

When it comes to Business Intelligence, reports are the name of the game. Building Dashboards and conducting Ad-hoc requests are examples of other Business Intelligence Deliverables. The final goal of Data Science deliverables is the same, but they emphasize long-term and forward-looking projects. Rather than using Business Visualization tools, projects will involve building models in production. These programs likewise prioritize forecasting future consequences above BI’s focus on the current state of a company.

Data Science and Business Intelligence Differences: Process

The contrast between the processes of each stems from the perspective of time, which shapes the character of deliverables in the same way. The initial step of analysis in Business Intelligence is Descriptive Analytics, which sets the stage for what has already occurred. Through visualizations, non-technical business users can grasp and interpret data. For example, instead of relying on direct website traffic, business managers can use promotional emails to figure out how many items X was sold in July. This prompts further investigation and analysis into why some channels worked better than others.

Using the previous item X as an example, Data Science would use an exploratory method. Instead of answering business questions about performance first, this implies studying the data through its Properties, Hypothesis Testing, and exploring common trends. Data Scientists frequently begin with a question or a hard problem, but this usually evolves as they investigate.

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Data Science and Business Intelligence Differences: Flexibility

Data Science is all about Flexibility as additional Data Sources can be added as needed in the future. Business Intelligence has very little flexibility. Estimating Data Sources should be done ahead of time. It’s also slow if you need to add more Data Sources.

Data Science and Business Intelligence Differences: Complexity

The scope of Business Intelligence is limited to the business domain. BI is concerned with the creation of Dashboards, the creation of Business Insights, the organization of data, and the extraction of information that will assist firms in growing thus making it less complex.

On the hand, Data Science acquires a much bigger picture. A wide range of advanced Statistical Techniques and Prediction Models are used in Data Science. Data Science is far more complex than Business Intelligence.

Data Science and Business Intelligence Differences: Skills

The following are some of the most significant skills required for Business Intelligence:

  • Possess good commercial acumen and creative thinking.
  • The ability to solve problems.
  • Data Analysis skills are necessary for making business judgments.
  • Communication and presenting skills are exceptional.
  • SQL data extraction capability.
  • In-depth knowledge of ETL (Extract, Transform, Load) tools comes in handy.

The abilities necessary for Data Science are as follows:

  • Proficiency in Python, R, SAS, and other technologies is needed.
  • Capable of performing complicated Statistical Data Analysis.
  • Working knowledge of Tableau, Matplotlib, ggplot2, and other Data Visualization tools are available.
  • It is necessary to work with both Structured and Unstructured data.
  • SQL and NoSQL skills are required.
  • Machine Learning algorithm knowledge is needed.
  • Knowledge of Big Data tools such as Hadoop and Spark.

Data Science and Business Intelligence Differences: Responsibilities

Working in Business Intelligence entails a variety of duties, including:

  • Engagement in business connectivity and identification of the Source System.
  • Concentrate on critical business areas and resolution plans.
  • Defining business needs in collaboration with project managers and clients.
  • Validation of the data is being done.
  • Putting approved proposals into action and achieving Strategic goals.
  • Reporting on the BI program’s progress.

The following are the responsibilities of a Data Scientist:

  • Data Preprocessing and Transformation are done by a Data Scientist.
  • Development of predictive models for future events.
  • Fine-tuning and improving the performance of Machine Learning Models.
  • Assisting industries in identifying questions that must be answered.
  • For visual communication of outcomes, story-telling is used.

How do Data Science and Business Intelligence work together?

Although both Data Science and Business Intelligence can provide important insight, combining the two delivers the most insight to drive strategic decisions. Consider the case of a professional services firm that has been having trouble winning proposals. Because they only have so many resources to respond to requests for proposals (RFPs), they decide to employ a data-driven process to determine which RFPs they are most likely to win.

The organization decides to use business intelligence to analyze past RFP results and generate customer and project profiles with a high win rate. The organization can then utilize Data Science and Machine Learning to construct numerous hypotheses and scenarios to forecast the likelihood of getting future projects. As a result of combining business intelligence and data science, the organization now has a data profile of clients and projects that are in their sweet spot for winning new business.

While both Data Science and Business Intelligence are utilized to make decisions, their perspectives are crucial in deciding how judgments are made. Data Science is frequently at the forefront of Strategic Planning and choosing future courses of action due to its forward-looking orientation. However, these judgments are frequently proactive rather than reactive. Business Intelligence, on the other hand, assists decision-making based on Historical Performance or Events. Both professions aim to provide information that will help businesses make better decisions, but the aspect of time separates them.

It’s easy to see how Data Science and Business Intelligence may both aid with insight, but it’s the combination of the two that provides the most value.

What does the future hold for Data Science and Business Intelligence?

In McKinsey’s blog post the influence of Machine Learning in at least 12 industry sectors was stated. According to the author, McKinsey has compelling data to show that Data Science, with its rich data (Big Data) and Advanced Analytics capabilities (Machine Learning), clearly outperforms traditional Business Intelligence, where Static or Historical Data did not provide users with sufficient justifications for “predicting” or “prescribing” future business events. Data Science and Machine Learning have aided the enterprise IT team by providing tools for making quick and accurate predictions based on historical Data Patterns.

According to McKinsey, an effective support structure for Enterprise Analytics activities, good architecture, and senior management involvement are all essential for an Enterprise Analytics platform to succeed. Businesses that have properly invested in Analytics and BI infrastructures have experienced up to a 19% rise in their margins over a five-year period, according to McKinsey.


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